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Record W4248756126 · doi:10.1504/ijicbm.2017.087310

Stakeholder initiatives for promoting corporate social responsibility: a four-country study

2017· article· en· W4248756126 on OpenAlexaboutno aff
Neha Saxena, Shamama Afreen

Bibliographic record

VenueInternational Journal of Indian Culture and Business Management · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityStakeholderPoliticsBusinessStakeholder theoryPublic relationsSocial responsibilityDeveloping countryCorporate governancePolitical scienceEconomic growthEconomics

Abstract

fetched live from OpenAlex

Corporate social responsibility (CSR) has undergone many stages of evolution around the world and different models exist in different countries. The focus and priorities of CSR also vary from one country to another, being rooted in the social, economic, cultural, political and historical landscape of each region. This, combined with pressures exerted by the expectations of an increasingly aware and connected world, is shaping the way different stakeholders respond to the CSR agenda of a country. Governments, corporations, non-governmental organisations, consumers and other important entities are coming together to design and implement effective CSR policies and practices, aimed at solving social and environmental problems. This paper looks at such stakeholder responses to the CSR agenda of four countries - Brazil, Germany, India and Canada - to understand existing CSR models and how they are being promoted and implemented.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.003
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.081
GPT teacher head0.316
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2017
Admission routes1
Has abstractyes

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